title: A wavelet-based method to exploit epigenomic language in the regulatory region.
abstract: BACKGROUND
  Epigenetic landscapes in the regulatory regions reflect binding condition of transcription factors and their co-factors. Identifying epigenetic condition and its variation is important in understanding condition-specific gene regulation. Computational approaches to explore complex multi-dimensional landscapes are needed.
  
  
  RESULTS
  To study epigenomic condition for gene regulation, we developed a method, AWNFR, to classify epigenomic landscapes based on the detected epigenomic landscapes. Assuming mixture of Gaussians for a nucleosome, the proposed method captures the shape of histone modification and identifies potential regulatory regions in the wavelet domain. For accuracy estimation as well as enhanced computational speed, we developed a novel algorithm based on down-sampling operation and footprint in wavelet. We showed the algorithmic advantages of AWNFR using the simulated data. AWNFR identified regulatory regions more effectively and accurately than the previous approaches with the epigenome data in mouse embryonic stem cells and human lung fibroblast cells (IMR90). Based on the detected epigenomic landscapes, AWNFR classified epigenomic status and studied epigenomic codes. We studied co-occurring histone marks and showed that AWNFR captures the epigenomic variation across time.
  
  
  METHODS
  The source code and supplemental document of AWNFR are available at http://wonk.med.upenn.edu/AWNFR.
authors.affiliation: Department of Genetics, Institute for Diabetes, Obesity and Metabolism, School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA and Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY 11030, USA.
authors.forename: Nha
authors.initials: N
authors.lastname: Nguyen
authors.forename: An
authors.initials: A
authors.lastname: Vo
authors.forename: Kyoung-Jae
authors.initials: KJ
authors.lastname: Won
mesh_terms: D000465:Algorithms; D000818:Animals; D002460:Cell Line; D016000:Cluster Analysis; D053595:Embryonic Stem Cells / Q000378:metabolism; D057890:Epigenomics / Q000379:methods*; D005347:Fibroblasts / Q000378:metabolism; D005786:Gene Expression Regulation; D006657:Histones / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D006801:Humans; D008168:Lung / Q000378:metabolism; D051379:Mice; D012985:Software Design
chemical_list: D006657:Histones